In this podcast episode, we talked with Tamara Atanasoska about ​building fair AI systems.

About the Speaker: ​Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at probable. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background. During the event, the guest discussed their career journey from software engineering to open-source contributions, focusing on explainability in AI through Scikit-learn and Fairlearn. They explored fairness in AI, including challenges in credit loans, hiring, and decision-making, and emphasized the importance of tools, human judgment, and collaboration. The guest also shared their involvement with PyLadies and encouraged contributions to Fairlearn. 0:00 Introduction to the event and the community 1:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI 2:37 Guest introduction: Tamara’s background and career 3:18 Tamara’s career journey: Software engineering, music tech, and computational linguistics 9:53 Tamara’s background in language and computer science 14:52 Exploring fairness in AI and its impact on society 21:20 Fairness in AI models 26:21 Automating fairness analysis in models 32:32 Balancing technical and domain expertise in decision-making 37:13 The role of humans in the loop for fairness 40:02 Joining Probable and working on open-source projects 46:20 Scopes library and its integration with Hugging Face 50:48 PyLadies and community involvement 55:41 The ethos of Scikit-learn and Fairlearn

🔗 CONNECT WITH TAMARA ATANASOSKA Linkedin - https://www.linkedin.com/in/tamaraatanasoska/ GitHub- https://github.com/TamaraAtanasoska

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